What is survival analysis and why should I learn it?
Survival analysis was originally developed and applied heavily by the actuarial and medical community. Its purpose was to answer why do events occur now versus later under uncertainty (where events might refer to deaths, disease remission, etc.). This is great for researchers who are interested in measuring lifetimes: they can answer questions like what factors might influence deaths?
But outside of medicine and actuarial science, there are many other interesting and exciting applications of this survival analysis. For example:
- SaaS providers are interested in measuring subscriber lifetimes, or time to some first action
- inventory stock out is a censoring event for true "demand" of a good.
- sociologists are interested in measuring political parties' lifetimes, or relationships, or marriages
- analyzing Godwin's law in Reddit comments
- A/B tests to determine how long it takes different groups to perform an action.
lifelines is a pure Python implementation of the best parts of survival analysis. We'd love to hear if you are using lifelines, please leave an Issue and let us know your thoughts on the library.
Installation:
You can install lifelines using
pip install lifelines
or conda install:
conda install -c conda-forge lifelines
Or getting the bleeding edge version with:
pip install --upgrade --no-deps git+https://github.com/CamDavidsonPilon/lifelines.git
from the command line.
Installation Issues?
See the common problems/solutions for installing lifelines.
lifelines documentation and an intro to survival analysis
If you are new to survival analysis, wondering why it is useful, or are interested in lifelines examples, API, and syntax, please check out the Documentation and Tutorials page
Example:
from lifelines import KaplanMeierFitter
durations = [11, 74, 71, 76, 28, 92, 89, 48, 90, 39, 63, 36, 54, 64, 34, 73, 94, 37, 56, 76]
event_observed = [True, True, False, True, True, True, True, False, False, True, True,
True, True, True, True, True, False, True, False, True]
kmf = KaplanMeierFitter()
kmf.fit(durations, event_observed)
kmf.plot()
Contacting & troubleshooting
- There is a Gitter channel available.
- Some users have posted common questions at stats.stackexchange.com
- creating an issue in the Github repository.
Roadmap
You can find the roadmap for lifelines here.
Development
See our Contributing guidelines.
Citing lifelines
You can use this badge below to generate a DOI and reference text for the latest related version of lifelines: